Уроки 6-9: autolog, hyperparam sweep, grid search, serving + MLproject, walkthrough

src/train_simple.py

@@ -15,7 +15,7 @@ import matplotlib.pyplot as plt
 import numpy as np
 from sklearn.datasets import load_digits
 from sklearn.ensemble import RandomForestClassifier
-from sklearn.metrics import accuracy_score, confusion_matrix, classification_report, recall_score, precision_score, f1_score
+from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
 from sklearn.model_selection import train_test_split
 
 import mlflow
@@ -66,9 +66,6 @@ def main():
 
         # Логируем метрики
         mlflow.log_metric("accuracy", acc)
-        mlflow.log_metric("recall_macro", recall_score(y_test, y_pred, average="macro"))
-        mlflow.log_metric("precision_macro", precision_score(y_test, y_pred, average="macro"))
-        mlflow.log_metric("f1_macro", f1_score(y_test, y_pred, average="macro"))
         # можно логировать несколько шагов (для графиков в UI)
         for i, tree in enumerate(model.estimators_):
             tree_acc = accuracy_score(y_test, tree.predict(X_test))